Annotate tumoral cells (case 3299)
2021/04/09
1 Introduction
Here, we will annotate the cells of the patient with id “3299”.
2 Pre-processing
2.1 Load packages
library(Seurat)
library(SeuratWrappers)
library(harmony)
library(tidyverse)2.2 Define parameters
# Paths
path_to_obj <- here::here("results/R_objects/5.seurat_clustered_3299.rds")
path_to_save <- here::here("results/R_objects/6.seurat_annotated_3299.rds")
path_to_save_markers <- here::here("3-clustering_and_annotation/markers_clusters_3299.rds")
# Colors
color_palette <- c("black", "gray", "red", "yellow", "violet", "green4",
"blue", "mediumorchid2", "coral2", "blueviolet",
"indianred4", "deepskyblue1", "dimgray", "deeppink1",
"green", "lightgray", "hotpink1")
# Source functions
source(here::here("bin/utils.R"))
# Thresholds
min_log2FC <- 0.3
alpha <- 0.0012.3 Load data
seurat <- readRDS(path_to_obj)
seurat## An object of class Seurat
## 23326 features across 6063 samples within 1 assay
## Active assay: RNA (23326 features, 2000 variable features)
## 3 dimensional reductions calculated: pca, harmony, umapDimPlot(seurat, cols = color_palette)
3 Cell Cycle Score
As upregulation of cell cycle genes is a hallmark of Richter transformation, we will infer the cell cycle score and phase for each cell:
seurat <- CellCycleScoring(
seurat,
s.features = cc.genes.updated.2019$s.genes,
g2m.features = cc.genes.updated.2019$g2m.genes,
set.ident = FALSE
)
DimPlot(seurat, group.by = "Phase")
umap_s_score <- FeaturePlot(seurat, features = "S.Score") +
scale_color_viridis_c(option = "magma") +
labs(title = "S Score") +
theme(
plot.title = element_text(hjust = 0.5, size = 12, face = "plain"),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()
)
umap_g2m_score <- FeaturePlot(seurat, features = "G2M.Score") +
scale_color_viridis_c(option = "magma") +
labs(title = "G2M Score") +
theme(
plot.title = element_text(hjust = 0.5, size = 12, face = "plain"),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()
)
umap_cc_combined <- ggpubr::ggarrange(
plotlist = list(umap_s_score, umap_g2m_score),
nrow = 2,
ncol = 1,
common.legend = FALSE
)
umap_cc_combined
4 Find Markers
markers <- FindAllMarkers(seurat, only.pos = TRUE, logfc.threshold = min_log2FC)
markers <- markers %>%
mutate(cluster = as.character(cluster)) %>%
filter(p_val_adj < alpha) %>%
arrange(cluster) %>%
group_by(cluster) %>%
arrange(desc(avg_log2FC), .by_group = TRUE)
DT::datatable(markers)5 Annotation
Important literature to annotate the cells:
- Intraclonal Complexity in Chronic Lymphocytic Leukemia:Fractions Enriched in Recently Born/Divided andOlder/Quiescent Cells.
- MicroRNA-155influences B-cell receptor signaling and associateswith aggressive disease in chronic lymphocytic leukemia.
- Of Lymph Nodes and CLL Cells: Deciphering the Role of CCR7 in the Pathogenesis of CLL and Understanding Its Potential as Therapeutic Target
| Cluster | Markers | Annotation |
|---|---|---|
| 0 | CXCR4, TCL1A, CD24 | CXCR4hiCD27lo |
| 1 | KLF6, PIK3IP1 | RT-like |
| 2 | CD27, S100A4, MS4A1 | CXCR4loCD27hi |
| 3 | MIR155HG, ENO1, MS4A1 | CD83loMIR155HGhi |
| 4 | MIR155HG, CD83, CD40, CCR7 | CD83hiMIR155HGhi |
Annotate:
seurat$annotation_final <- factor(
seurat$final_clusters,
levels = c("0", "1", "2", "3", "4")
)
new_levels_3299 <- c("CXCR4hiCD27lo", "RT-like", "CXCR4loCD27hi",
"CD83loMIR155HGhi", "CD83hiMIR155HGhi")
levels(seurat$annotation_final) <- new_levels_3299
reordered_levels_3299 <- c("CXCR4hiCD27lo", "CXCR4loCD27hi", "CD83loMIR155HGhi",
"CD83hiMIR155HGhi", "RT-like")
seurat$annotation_final <- factor(seurat$annotation_final, reordered_levels_3299)
Idents(seurat) <- "annotation_final"
# Plot UMAP
cols <- c("gray79", "#9d9fa1", "gray30", "#f6c7c4", "#c88495")
names(cols) <- levels(seurat$annotation_final)
umap_annotation <- DimPlot(seurat, pt.size = 0.5)
col_labels <- c(
"CXCR4hiCD27lo" = bquote(CXCR4^hi~CD27^lo),
"CXCR4loCD27hi" = bquote(CXCR4^lo~CD27^hi),
"CD83loMIR155HGhi" = bquote(CD83^lo~MIR155HG^hi),
"CD83hiMIR155HGhi" = bquote(CD83^hi~MIR155HG^hi),
"RT-like" = bquote("RT-like")
)
umap_annotation <- umap_annotation +
scale_color_manual(values = cols, breaks = names(cols), labels = col_labels) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()
)
umap_annotation
6 Visualize markers
# UMAPs
genes_interest <- c("CXCR4", "CD24", "CD27", "S100A4", "MS4A1",
"MIR155HG", "CD83", "ENO1", "CD40", "CCR7", "KLF6",
"PIK3IP1")
feature_plots <- purrr::map(genes_interest, function(x) {
p <- FeaturePlot(seurat, x, pt.size = 0.5) +
scale_color_viridis_c(option = "magma")
p
})
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# Dot plots
dot_plot <- DotPlot(seurat, features = rev(genes_interest)) +
coord_flip() +
scale_color_viridis_c(option = "magma") +
scale_y_discrete(breaks = names(col_labels), labels = col_labels) +
theme(
axis.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
legend.title = element_text(size = 12)
)
dot_plot
# Violin plots
vln_plot_s <- seurat@meta.data %>%
ggplot(aes(annotation_final, S.Score)) +
geom_violin(fill = "gray") +
labs(x = "", y = "S Phase Score") +
scale_x_discrete(breaks = names(col_labels), labels = col_labels) +
theme_bw() +
theme(axis.text.x = element_text(color = "black", angle = 45, vjust = 1, hjust = 1, size = 11))
vln_plot_s
vln_plot_g2m <- seurat@meta.data %>%
ggplot(aes(annotation_final, G2M.Score)) +
geom_violin(fill = "gray") +
labs(x = "", y = "G2M Phase Score") +
scale_x_discrete(breaks = names(col_labels), labels = col_labels) +
theme_bw() +
theme(axis.text.x = element_text(color = "black", angle = 45, vjust = 1, hjust = 1, size = 11))
vln_plot_g2m
7 Save
# Save Seurat object
saveRDS(seurat, path_to_save)
# Save markers
markers$annotation <- factor(markers$cluster)
levels(markers$annotation) <- new_levels_3299
markers_list <- purrr::map(levels(markers$annotation), function(x) {
df <- markers[markers$annotation == x, ]
df <- df[, c(7, 1, 5, 2:4, 6, 8)]
df
})
names(markers_list) <- levels(markers$annotation)
markers_list <- markers_list[reordered_levels_3299]
markers_final <- bind_rows(markers_list)
saveRDS(markers_list, path_to_save_markers)
saveRDS(
markers_final,
here::here("results/tables/markers/markers_annotated_clusters_patient_3299.rds")
)
saveRDS(markers_list, path_to_save_markers)
openxlsx::write.xlsx(
x = markers_list,
file = "results/tables/markers/markers_annotated_clusters_patient_3299.xlsx"
)8 Session Info
sessionInfo()## R version 4.0.4 (2021-02-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=es_ES.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=es_ES.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.3 tidyverse_1.3.1 harmony_1.0 Rcpp_1.0.6 SeuratWrappers_0.3.0 SeuratObject_4.0.2 Seurat_4.0.3 BiocStyle_2.18.1
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1 plyr_1.8.6 igraph_1.2.6 lazyeval_0.2.2 splines_4.0.4 crosstalk_1.1.1 listenv_0.8.0 scattermore_0.7 digest_0.6.27 htmltools_0.5.1.1 fansi_0.5.0 magrittr_2.0.1 tensor_1.5 cluster_2.1.1 ROCR_1.0-11 limma_3.46.0 openxlsx_4.2.3 remotes_2.4.0 globals_0.14.0 modelr_0.1.8 matrixStats_0.59.0 spatstat.sparse_2.0-0 colorspace_2.0-1 rvest_1.0.0 ggrepel_0.9.1 haven_2.4.1 xfun_0.23 crayon_1.4.1 jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-10 zoo_1.8-9 glue_1.4.2 polyclip_1.10-0 gtable_0.3.0 leiden_0.3.8 car_3.0-10 future.apply_1.7.0 abind_1.4-5 scales_1.1.1 DBI_1.1.1 rstatix_0.7.0 miniUI_0.1.1.1 viridisLite_0.4.0 xtable_1.8-4 reticulate_1.20 spatstat.core_2.1-2 foreign_0.8-81 rsvd_1.0.5 DT_0.18 htmlwidgets_1.5.3 httr_1.4.2 RColorBrewer_1.1-2
## [55] ellipsis_0.3.2 ica_1.0-2 farver_2.1.0 pkgconfig_2.0.3 sass_0.4.0 uwot_0.1.10 dbplyr_2.1.1 deldir_0.2-10 here_1.0.1 utf8_1.2.1 labeling_0.4.2 tidyselect_1.1.1 rlang_0.4.11 reshape2_1.4.4 later_1.2.0 munsell_0.5.0 cellranger_1.1.0 tools_4.0.4 cli_2.5.0 generics_0.1.0 broom_0.7.7 ggridges_0.5.3 evaluate_0.14 fastmap_1.1.0 yaml_2.2.1 goftest_1.2-2 knitr_1.33 fs_1.5.0 fitdistrplus_1.1-5 zip_2.2.0 RANN_2.6.1 pbapply_1.4-3 future_1.21.0 nlme_3.1-152 mime_0.10 xml2_1.3.2 compiler_4.0.4 rstudioapi_0.13 curl_4.3.1 plotly_4.9.4 png_0.1-7 ggsignif_0.6.2 spatstat.utils_2.2-0 reprex_2.0.0 bslib_0.2.5.1 stringi_1.6.2 highr_0.9 lattice_0.20-41 Matrix_1.3-4 vctrs_0.3.8 pillar_1.6.1 lifecycle_1.0.0 BiocManager_1.30.15 spatstat.geom_2.1-0
## [109] lmtest_0.9-38 jquerylib_0.1.4 RcppAnnoy_0.0.18 data.table_1.14.0 cowplot_1.1.1 irlba_2.3.3 httpuv_1.6.1 patchwork_1.1.1 R6_2.5.0 bookdown_0.22 promises_1.2.0.1 rio_0.5.26 KernSmooth_2.23-18 gridExtra_2.3 parallelly_1.26.0 codetools_0.2-18 MASS_7.3-53.1 assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.2 sctransform_0.3.2 mgcv_1.8-36 parallel_4.0.4 hms_1.1.0 grid_4.0.4 rpart_4.1-15 rmarkdown_2.8 carData_3.0-4 Rtsne_0.15 ggpubr_0.4.0 shiny_1.6.0 lubridate_1.7.10